Web Import now uses dataprep
This commit is contained in:
Sander Roosendaal
@@ -217,6 +217,116 @@ def timedeltaconv(x):
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return dt
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# Processes painsled CSV file to database
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def save_workout_database(f2,r,dosmooth=True,workouttype='rower',
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dosummary=True,title='Workout',
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notes='',totaldist=0,totaltime=0):
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message = None
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powerperc = 100*np.array([r.pw_ut2,
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r.pw_ut1,
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r.pw_at,
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r.pw_tr,r.pw_an])/r.ftp
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# make workout and put in database
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rr = rrower(hrmax=r.max,hrut2=r.ut2,
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hrut1=r.ut1,hrat=r.at,
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hrtr=r.tr,hran=r.an,ftp=r.ftp,
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powerperc=powerperc,powerzones=r.powerzones)
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row = rdata(f2,rower=rr)
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if row == 0:
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return (0,'Error: CSV data file not found')
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if dosmooth:
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# auto smoothing
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pace = row.df[' Stroke500mPace (sec/500m)'].values
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velo = 500./pace
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f = row.df['TimeStamp (sec)'].diff().mean()
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windowsize = 2*(int(10./(f)))+1
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if not 'originalvelo' in row.df:
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row.df['originalvelo'] = velo
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if windowsize > 3 and windowsize<len(velo):
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velo2 = savgol_filter(velo,windowsize,3)
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else:
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velo2 = velo
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velo3 = pd.Series(velo2)
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velo3 = velo3.replace([-np.inf,np.inf],np.nan)
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velo3 = velo3.fillna(method='ffill')
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pace2 = 500./abs(velo3)
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row.df[' Stroke500mPace (sec/500m)'] = pace2
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row.df = row.df.fillna(0)
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row.write_csv(f2,gzip=True)
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try:
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os.remove(f2)
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except:
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pass
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# recalculate power data
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if workouttype == 'rower' or workouttype == 'dynamic' or workouttype == 'slides':
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try:
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row.erg_recalculatepower()
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row.write_csv(f2,gzip=True)
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except:
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pass
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averagehr = row.df[' HRCur (bpm)'].mean()
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maxhr = row.df[' HRCur (bpm)'].max()
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if totaldist == 0:
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totaldist = row.df['cum_dist'].max()
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if totaltime == 0:
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totaltime = row.df['TimeStamp (sec)'].max()-row.df['TimeStamp (sec)'].min()
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totaltime = totaltime+row.df.ix[0,' ElapsedTime (sec)']
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hours = int(totaltime/3600.)
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if hours>23:
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message = 'Warning: The workout duration was longer than 23 hours'
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hours = 23
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minutes = int((totaltime - 3600.*hours)/60.)
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seconds = int(totaltime - 3600.*hours - 60.*minutes)
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tenths = int(10*(totaltime - 3600.*hours - 60.*minutes - seconds))
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duration = "%s:%s:%s.%s" % (hours,minutes,seconds,tenths)
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if dosummary:
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summary = row.summary()
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summary += '\n'
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summary += row.intervalstats()
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workoutdate = row.rowdatetime.strftime('%Y-%m-%d')
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workoutstarttime = row.rowdatetime.strftime('%H:%M:%S')
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workoutstartdatetime = thetimezone.localize(row.rowdatetime).astimezone(utc)
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# check for duplicate start times
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ws = Workout.objects.filter(starttime=workoutstarttime,
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user=r)
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if (len(ws) != 0):
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message = "Warning: This workout probably already exists in the database"
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w = Workout(user=r,name=title,date=workoutdate,
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workouttype=workouttype,
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duration=duration,distance=totaldist,
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weightcategory=r.weightcategory,
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starttime=workoutstarttime,
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csvfilename=f2,notes=notes,summary=summary,
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maxhr=maxhr,averagehr=averagehr,
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startdatetime=workoutstartdatetime)
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w.save()
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# put stroke data in database
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res = dataprep(row.df,id=w.id,bands=True,
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barchart=True,otwpower=True,empower=True)
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return (w.id,message)
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# Create new workout from file and store it in the database
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# This routine should be used everywhere in views.py and mailprocessing.py
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# Currently there is code duplication
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@@ -336,105 +446,14 @@ def new_workout_from_file(r,f2,
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except:
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os.remove(f_to_be_deleted+'.gz')
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powerperc = 100*np.array([r.pw_ut2,
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r.pw_ut1,
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r.pw_at,
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r.pw_tr,r.pw_an])/r.ftp
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# make workout and put in database
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rr = rrower(hrmax=r.max,hrut2=r.ut2,
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hrut1=r.ut1,hrat=r.at,
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hrtr=r.tr,hran=r.an,ftp=r.ftp,
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powerperc=powerperc,powerzones=r.powerzones)
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row = rdata(f2,rower=rr)
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if row == 0:
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return (0,'Error: CSV data file not found')
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dosummary = (fileformat != 'fit')
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id,message = save_workout_database(f2,r,
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workouttype=workouttype,
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dosummary=dosummary,
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title=title)
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# auto smoothing
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pace = row.df[' Stroke500mPace (sec/500m)'].values
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velo = 500./pace
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f = row.df['TimeStamp (sec)'].diff().mean()
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windowsize = 2*(int(10./(f)))+1
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if not 'originalvelo' in row.df:
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row.df['originalvelo'] = velo
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if windowsize > 3 and windowsize<len(velo):
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velo2 = savgol_filter(velo,windowsize,3)
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else:
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velo2 = velo
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velo3 = pd.Series(velo2)
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velo3 = velo3.replace([-np.inf,np.inf],np.nan)
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velo3 = velo3.fillna(method='ffill')
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pace2 = 500./abs(velo3)
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row.df[' Stroke500mPace (sec/500m)'] = pace2
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row.df = row.df.fillna(0)
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row.write_csv(f2,gzip=True)
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try:
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os.remove(f2)
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except:
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pass
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# recalculate power data
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if workouttype == 'rower' or workouttype == 'dynamic' or workouttype == 'slides':
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try:
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row.erg_recalculatepower()
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row.write_csv(f2,gzip=True)
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except:
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pass
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if fileformat != 'fit' and summary == '':
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summary = row.summary()
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summary += '\n'
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summary += row.intervalstats_painsled()
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averagehr = row.df[' HRCur (bpm)'].mean()
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maxhr = row.df[' HRCur (bpm)'].max()
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totaldist = row.df['cum_dist'].max()
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totaltime = row.df['TimeStamp (sec)'].max()-row.df['TimeStamp (sec)'].min()
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totaltime = totaltime+row.df.ix[0,' ElapsedTime (sec)']
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hours = int(totaltime/3600.)
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if hours>23:
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message = 'Warning: The workout duration was longer than 23 hours'
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hours = 23
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minutes = int((totaltime - 3600.*hours)/60.)
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seconds = int(totaltime - 3600.*hours - 60.*minutes)
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tenths = int(10*(totaltime - 3600.*hours - 60.*minutes - seconds))
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duration = "%s:%s:%s.%s" % (hours,minutes,seconds,tenths)
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workoutdate = row.rowdatetime.strftime('%Y-%m-%d')
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workoutstarttime = row.rowdatetime.strftime('%H:%M:%S')
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workoutstartdatetime = thetimezone.localize(row.rowdatetime).astimezone(utc)
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# check for duplicate start times
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ws = Workout.objects.filter(starttime=workoutstarttime,
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user=r)
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if (len(ws) != 0):
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message = "Warning: This workout probably already exists in the database"
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w = Workout(user=r,name=title,date=workoutdate,
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workouttype=workouttype,
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duration=duration,distance=totaldist,
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weightcategory=r.weightcategory,
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starttime=workoutstarttime,
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csvfilename=f2,notes=notes,summary=summary,
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maxhr=maxhr,averagehr=averagehr,
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startdatetime=workoutstartdatetime)
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w.save()
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# put stroke data in database
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res = dataprep(row.df,id=w.id,bands=True,
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barchart=True,otwpower=True,empower=True)
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return (w.id,message)
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return (id,message)
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# Compare the data from the CSV file and the database
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# Currently only calculates number of strokes. To be expanded with
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